{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decompiler and Circuit Optimizer\n",
    "\n",
    "Qlasskit offers two useful tool for circuit analysis and optimization.\n",
    "\n",
    "- Decompiler: given a quantum circuit is able to detect section that can be represented as boolean expressions\n",
    "- circuit_boolean_optimizer: a pipeline that given a quantum circuit, decompose it in boolean expressions form and optimize it using boolean algebra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first write a qlasskit function that perform an And between the elements of a Qlist; we use the `fastOptimizer` in order to obtain an unoptimized circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QCircuit<qf>(7 gates, 6 qubits)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qlasskit import qlassfa, qlassf, boolopt, Qlist\n",
    "from qlasskit.decompiler import Decompiler, circuit_boolean_optimizer\n",
    "\n",
    "\n",
    "@qlassfa(bool_optimizer=boolopt.fastOptimizer)\n",
    "def qf(a: Qlist[bool, 2]) -> bool:\n",
    "    s = True\n",
    "    for i in a:\n",
    "        s = s and i\n",
    "    return s\n",
    "\n",
    "\n",
    "qf.circuit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the circuit, this is not the best solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.552x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qf.export().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the decompiler we are able to translate a quantum circuit to its boolean representation (if applicable):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecompiledResults[\n",
       "\t(\n",
       "\t\t(0, 7)\n",
       "\t\t(X, [2], None), (CCX, [0, 2, 3], None), (CCX, [1, 3, 4], None), (CX, [4, 5], None), (CCX, [1, 3, 4], None), (CCX, [0, 2, 3], None), (X, [2], None)\n",
       "\t\t(q5, q4 ^ q5 ^ (q1 & (q3 ^ (q0 & ~q2))))\n",
       "\t)\n",
       "]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc = Decompiler().decompile(qf.circuit())\n",
    "dc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `circuit_boolean_optimizer` allows us to perform boolean optimizations in a quantum circuit; from the previous unoptimized example, we get the following optimized circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 203.885x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = circuit_boolean_optimizer(qf.circuit(), preserve=[0, 1])\n",
    "qc.export().draw(\"mpl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qlasskit_310-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}